Triple

T30718877
Position Surface form Disambiguated ID Type / Status
Subject Nadie Sabe Lo Que Va a Pasar Mañana E782095 entity
Predicate hasTrack P3284 FINISHED
Object Where She Goes
"Where She Goes" is a 2023 reggaeton-trap single by Puerto Rican artist Bad Bunny that blends atmospheric production with introspective lyrics and became a global hit.
 E1928284 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Where She Goes | Statement: [Nadie Sabe Lo Que Va a Pasar Mañana, hasTrack, Where She Goes]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Where She Goes
Triple: [Nadie Sabe Lo Que Va a Pasar Mañana, hasTrack, Where She Goes]
Generated description
"Where She Goes" is a 2023 reggaeton-trap single by Puerto Rican artist Bad Bunny that blends atmospheric production with introspective lyrics and became a global hit.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f224acd24481908ed5f96f0d69b5dd completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68c55fe2481908a6af9689dde89f3 completed May 2, 2026, 11:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a289907a57c8190b95cd0bf5fffacab completed June 9, 2026, 10:51 p.m.
NEDg Description generation batch_6a2899a5f87881909200941832511700 completed June 9, 2026, 10:54 p.m.
NED2 Entity disambiguation (via description) batch_6a289ac7f570819094b7940133c5ac52 completed June 9, 2026, 10:59 p.m.
Created at: April 29, 2026, 8:36 p.m.